Identification of FDFT1 and PGRMC1 as New Biomarkers in Nonalcoholic Steatohepatitis (NASH)-Related Hepatocellular Carcinoma by Deep Learning

Qiqi Liu,1 Yinuo Yang,1 Yongshuai Wang,2 Shuhang Wei,1 Liu Yang,1 Tiantian Liu,1 Zhen Yu,3 Yuemin Feng,3 Ping Yao,4 Qiang Zhu1,5 1Department of Gastroenterology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People’s Republic of China; 2School of Computer Science and Technology, Shandong University, Qingdao, Shandong, 266237, People’s Republic of China; 3Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China; 4Department of Gastroenterology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830000, People’s Republic of China; 5Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of ChinaCorrespondence: Qiang Zhu, Department of Gastroenterology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University; Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China, Email [email protected] Ping Yao, Department of Gastroenterology, the First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan Road, Urumqi, Xinjiang, 830000, People’s Republic of China, Email [email protected]: With the global epidemic of obesity and diabetes, non-alcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease, and NASH is increasingly becoming a major risk factor for hepatocellular carcinoma. Therefore, it is essential to explore novel biomarkers in NASH-related HCC.Methods: Deep Learning (DL) methods are a promising and encouraging tool widely used in genomics by automatically applying neural networks (NNs). Therefore, DL, “limma package”, weighted gene co-expression network analysis (WGCNA), and Protein–Protein Interaction Networks (PPI) were used to screen feature genes. Real-time quantitative PCR was used to validate the expression of feature genes in the NAFLD mice model. Enrichment and single-cell sequencing analyses of single genes were performed to investigate the role of feature genes in NASH-related HCC.Results: Combined core genes screened by DL in NAFLD with important genes in metabolic syndrome, six feature genes (FDFT1, TNFSF10, DNAJC16, RDH11, PGRMC1, and MYC) were obtained. ROC analysis demonstrates the model’s superiority with the AUC was 0.983 (0.9241– 0.98885). Animal experiments based on NAFLD mouse models have also shown that FDFT1, TNFSF10, DNAJC16, RDH11, and PGRMC1 have a higher expression in NAFLD livers. Among the feature genes, FDFT1 and PGRMC1 showed significant expression trends and outstanding diagnosis value in NASH-HCC.Conclusion: In conclusion, FDFT1 and PGRMC1 are key enzymes in the cholesterol synthesis pathway, our study validates the important role of cholesterol metabolism in NAFLD from another perspective, implying they may be new prognostic and diagnostic markers for NASH-HCC. Keywords: NAFLD, NASH-HCC, deep learning, biomarkers, metabolic syndrome, cholesterol metabolism